This invention relates to a method for organizing, updating and helping determine which "patterns" or associations amongst data in a database are of interest to a user of the database.
One of the central and most basic problems in the field of "knowledge discovery" is that of determining which patterns or associations amongst data in a database are of interest to a user of the database. As the literature has stated (see, e.g., G. Piatetsky-Shapiro and C. J. Matheus, "The Interestingness of Deviations," Proceedings of the AAAI-94Workshop on Knowledge Discovery in Databases, 25-36, 1994) one way of gauging a user's interest in a pattern, particularly in a business context, is to determine whether and how a user wishes to act on a pattern. Patterns that satisfy this criterion are called "actionable" patterns. G. Piatetsky-Shapiro and C. J. Matheus, "The Interestingness of Deviations," Proceedings of the AAAI-94 Workshop on Knowledge Discovery in Databases, 25-36, 1994; A. Silberschatz and A. Tuzhilin, "On subjective measures of Interestingness in knowledge discovery," Proceedings of the First International Conference on Knowledge Discovery and Data Mining, Montreal, Canada, August, 1995.
For example, consider a retail outlet or supermarket which wants to maximize its profit. In order to do so, it may want to take certain promotional, advertising or inventory stocking measures in response to certain facts (i.e., reflected as patterns or associations in the supermarket's database). For example, if a supermarket's database reflects that more of its customers now have children age six or under, and the database also reflects the fact that such customers in the past have bought more sweets, the supermarket will likely wish to stock up on sweets. But in order for the supermarket to be able to act on such information, it must be able to: (1) specify such associations between facts of import to it (i.e, specify which patterns are of interest); (2) associate such patterns with actions the supermarket would like to take in the event such patterns (associations of facts) arise; (3) periodically check, in for example a database, to determine whether such interesting patterns have in fact arose, and if so, act upon them; and (4) periodically update and change the supermarket's database to reflect the emergence of new facts and the disappearance of old ones.
These are difficult tasks. In particular, listing all possible actions for a given application and associating these actions with various patterns may be a huge endeavor. There may be many different actions for a given application, and it can be difficult (or even impossible) to list all of them in advance. In addition, even if all possible actions are listed, the actions still have to be assigned to various groups of patterns, and this can also be an overwhelming task.
In addition, periodically checking a database to determine whether user-specified patterns of interest have in fact arose can involve large computational resources.
Thus, what is needed is a method for allowing a database user to specify a potentially large number of (1) interesting patterns in the database, (2) actions to take in response, (3) and associations between the actions and patterns in an easy, efficient and intuitive manner. The method should also provide a way to determine whether new user-specified patterns of interest have arose so that on the one hand the system (and therefore the user) knows whenever new patterns satisfying the user-specified criterion have emerged, but on the other hand, time and computational resources are spared to the greatest degree possible.